CN109034254B - Method, system and storage medium for customizing artificial intelligence online service - Google Patents
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Abstract
The invention provides a method, a system and a storage medium for customizing artificial intelligence online service. The method comprises the following steps: the method comprises the steps of algorithm storage, wherein a plurality of algorithm data formats, a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images are obtained from an algorithm provider and stored; a data storage step, namely acquiring a plurality of training data from an algorithm demand side and storing the training data; a taking-out step, namely taking out first training data from the plurality of training data, and taking out a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images; training, namely training first training data by using a first training algorithm container mirror image to obtain a training result; and a combination step, namely combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
Description
Technical Field
The invention relates to a method, a system and a storage medium for customizing an artificial intelligence online service.
Background
Artificial Intelligence (AI) technology has gradually entered the field of view of people's lives. Building artificial intelligence services generally involves three steps: big data collection and processing, AI algorithm model training and AI algorithm online service. Wherein the data collection and processing facilitates the design and training of the AI algorithm by processing both structured data and unstructured data. The AI algorithm model training is an AI algorithm with design features, and the AI algorithm model is trained through a large amount of data to obtain an algorithm model with a certain function (for example, face recognition and voice recognition). The AI algorithm is online serviced, that is, the AI algorithm model is online deployed to be able to operate as a service (e.g., a face recognition service, a voice recognition service).
In the field of artificial intelligence at present, a situation exists, that is, a large number of traditional companies (including traditional enterprises, internet enterprises and the like) have different artificial intelligence application scenarios and massive data, but lack good artificial intelligence algorithm development and training capabilities. Some companies with powerful artificial intelligence algorithm development capabilities cannot access the data of traditional companies on one hand, and on the other hand do not have enough manpower to serve a large number of companies with algorithm requirements.
Common frameworks currently used to train and use AI algorithmic models include TensorFlow, Caffe, MXNet, Torch, and others.
A one-stop machine learning algorithm and deep learning algorithm service platform integrating data processing, algorithm selection and online service is provided by a foreign manufacturer Google based on a Tensorflow framework.
(https://cloud.google.com/ml-engine/)
A machine learning PAI platform provided by Ariicloud of a domestic manufacturer provides a one-stop machine learning algorithm service platform integrating data processing, algorithm selection and online service.
(https://data.aliyun.com/product/learnspm＝a2c0j.103967.416540.116.0hQEYB)
A domestic manufacturer Tengcong cloud provides a DI-X deep learning platform, and provides a one-stop deep learning algorithm service platform integrating data processing, algorithm selection and online service.
(https://www.qcloud.com/product/dix#scenarios)
The cloud provides a one-stop deep learning algorithm service platform which is based on a Kubernetes + Tensflow architecture and integrates AI training and online service. (https:// caiclone. io/products/taas)
The deep learning or machine learning platform proposed by Google, aristoloc and Tencent cloud aims at a whole set of one-stop services from data processing, algorithm selection and algorithm training to final online service, and the provider of the model algorithm is either Google, aristoloc and Tencent cloud (namely, a single algorithm provider) or the company owning the data uses the artificial intelligence algorithm developed by the company (namely, the algorithm provider and the user are the same). Therefore, the platform cannot achieve the aim that a large number of traditional companies use the algorithm models provided by different third-party artificial intelligence algorithm companies to train own data and apply the data to practice.
The cloud-provided one-stop deep learning algorithm service platform is only a privatized Tensorflow management platform and does not provide algorithm service, so that the cloud-provided one-stop deep learning algorithm service platform does not have the capability of enabling a traditional company to train own data by using an algorithm model provided by an artificial intelligence algorithm company and applying the data to practice.
Disclosure of Invention
In view of the above problems, the present invention provides a method and system for customizing an artificial intelligence online service, which can conveniently enable a traditional company to train its own data by using an algorithm model provided by an artificial intelligence algorithm company and apply the data to practice.
The invention provides a method for customizing artificial intelligence online service, which comprises the following steps:
the method comprises the steps of algorithm storage, wherein a plurality of algorithm data formats, a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images are obtained from an algorithm provider and stored in an algorithm storage unit in a cloud platform;
a data storage step, namely acquiring a plurality of training data from an algorithm demand party, and storing the training data in a data storage unit in the cloud platform, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a taking-out step of taking out first training data from the plurality of training data stored in the data storage unit, and taking out a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
training, namely training the first training data by using the first training algorithm container mirror image to obtain a training result;
and a combination step, combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
The algorithm provider provides a plurality of artificial intelligence algorithms and a plurality of algorithm data formats, and modifies the artificial intelligence algorithms according to algorithm container mirror image specifications provided by the cloud platform to obtain a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images.
Wherein the algorithm demander provides corresponding training data according to the corresponding algorithm data format selected from the plurality of algorithm data formats, thereby providing the plurality of training data.
Wherein the algorithm demander cannot acquire a plurality of training algorithm container images and a plurality of online service algorithm container images from the algorithm storage unit, and the algorithm provider cannot acquire the plurality of training data from the data storage unit.
The invention also provides a system for customizing the artificial intelligence online service, which comprises an algorithm provider, an algorithm demander and a cloud platform, wherein,
the cloud platform includes:
the algorithm storage unit acquires and stores a plurality of algorithm data formats, a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images from the algorithm provider;
the data storage unit is used for acquiring a plurality of training data from the algorithm demand party and storing the training data, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a fetching unit that fetches first training data from the plurality of training data stored in the data storage unit, and fetches a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
the training unit is used for training the first training data by using the first training algorithm container mirror image to obtain a training result;
and the combination unit is used for combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
The present invention further provides a non-volatile storage medium having stored thereon a program for customizing an artificial intelligence online service, the program being executed by a computer to implement a method of customizing an artificial intelligence online service, the program comprising:
the method comprises the steps that an algorithm storage instruction is obtained from an algorithm provider, and a plurality of algorithm data formats, a plurality of training algorithm container images and a plurality of online service algorithm container images are stored in an algorithm storage unit in a cloud platform;
a data storage instruction, which is used for acquiring a plurality of training data from an algorithm demand party and storing the training data in a data storage unit in the cloud platform, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a fetching instruction, which is used for fetching first training data from the plurality of training data stored in the data storage unit, and fetching a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
training instructions, training the first training data by using the first training algorithm container mirror image to obtain a training result; and combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
In the invention, an algorithm demander (namely, a traditional company) provides training data, an algorithm provider (namely, an artificial intelligence algorithm company) provides different algorithms, and the training data provided by the algorithm demander can be trained and combined through a cloud platform, so that artificial intelligence online service can be customized for the algorithm demander. Therefore, the invention can enable an algorithm demander to train own data by using the algorithm model provided by the artificial intelligence algorithm company and apply the data to practice.
Drawings
FIG. 1 is a block diagram of a system for customizing an artificial intelligence online service, in accordance with an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of customizing an artificial intelligence online service, according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a system for customizing an artificial intelligence online service according to an embodiment of the present invention, as shown in fig. 1, the system including an algorithm provider 1, an algorithm demander 2, and a cloud platform 3. The cloud platform 3 includes an algorithm storage unit 34, a data storage unit 35, a retrieval unit 31, a training unit 32, and a combination unit 33.
FIG. 2 is a flow diagram of a method of customizing an artificial intelligence online service, according to an embodiment of the present invention. An embodiment of the present invention will be described in detail with reference to fig. 1 and 2.
In the algorithm storing step S21 of fig. 2, the algorithm storage unit 34 acquires and stores a plurality of algorithm data formats, a plurality of training algorithm container images, and a plurality of online service algorithm container images from the algorithm provider 1.
Here, the algorithm provider 1 provides a plurality of artificial intelligence algorithms and a plurality of algorithm data formats, and modifies the plurality of artificial intelligence algorithms according to the algorithm container mirror specification provided by the cloud platform 3 to obtain a plurality of training algorithm container mirrors and a plurality of online service algorithm container mirrors. The algorithm provider 1 obtains an algorithm container mirror image specification from an algorithm storage unit 34 in the cloud platform 3, and modifies an artificial intelligence algorithm according to the algorithm container mirror image specification to obtain a corresponding training algorithm container mirror image and a corresponding online service algorithm container mirror image. The algorithm provider 1 then provides the algorithm data format, the corresponding training algorithm container image, and the corresponding online service algorithm container image to the cloud platform 3 and stores in the algorithm storage unit 34.
The algorithm provider 1 in the present invention may be a plurality of different algorithm providers, and therefore, a plurality of algorithm data formats, a plurality of training algorithm container images, and a plurality of online service algorithm container images as described above are stored in the algorithm storage unit 34.
In the data storage step S22, the data storage unit 35 acquires and stores a plurality of training data from the algorithm demander 2, and the data formats of the plurality of training data respectively conform to the corresponding algorithm data formats of the plurality of algorithm data formats. The algorithm demander 2 provides corresponding training data according to a corresponding algorithm data format selected from the plurality of algorithm data formats, thereby providing a plurality of training data.
Here, for example, the algorithm demanding party 2 selects one algorithm data format to be used from among a plurality of algorithm data formats stored in the algorithm storage unit 34, and then provides training data corresponding to this algorithm data format according to this algorithm data format. Thus, the data format of the training data provided by the algorithm demander 2 conforms to the algorithm data format.
The algorithm demander 2 in the present invention may be a plurality of different algorithm demanders, and these algorithm demanders may select different algorithm data formats to be used from the algorithm storage unit 34 according to different requirements, and provide corresponding training data according to the selected algorithm data formats. As such, a plurality of training data as described above may be provided.
Next, in a fetching step S23, the fetching unit 31 fetches the first training data from the plurality of training data stored in the data storage unit 35, and fetches the first training algorithm container mirror image and the first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirrors and the plurality of online service algorithm container mirrors stored in the algorithm storage unit 34.
Here, for each training data stored in the data storage unit 35, a training algorithm container image and an online service algorithm container image corresponding thereto can be found in the algorithm storage unit 34.
In training step S24, training unit 32 trains the first training data using the first training algorithm container image to obtain a training result.
Then, in a combining step S25, the combining unit 33 combines the first online service algorithm container mirror image and the training result obtained by the training to obtain the customized artificial intelligence online service.
In addition, the algorithm demander 2 cannot acquire the plurality of training algorithm container images and the plurality of online service algorithm container images from the algorithm storage unit 34 in the cloud platform 3, and the algorithm provider 1 cannot acquire the plurality of training data from the data storage unit 35. That is to say, the algorithm demander 2 cannot directly acquire the specific algorithm provided by the algorithm provider 1, and the algorithm provider 1 cannot directly acquire the specific data provided by the algorithm demander 2, so that the security of the algorithm and the data is ensured.
In the invention, an algorithm demander (namely, a traditional company) provides training data, an algorithm provider (namely, an artificial intelligence algorithm company) provides different algorithms, and the training data provided by the algorithm demander can be trained and combined through a cloud platform, so that artificial intelligence online service can be customized for the algorithm demander. Therefore, the invention can enable an algorithm demander to train own data by using the algorithm model provided by the artificial intelligence algorithm company and apply the data to practice.
The present invention also provides a nonvolatile storage medium on which a program for customizing an artificial intelligence online service is stored, the program being executed by a computer to implement a method of customizing an artificial intelligence online service, the program comprising:
the method comprises the steps that an algorithm storage instruction is obtained from an algorithm provider, and a plurality of algorithm data formats, a plurality of training algorithm container images and a plurality of online service algorithm container images are stored in an algorithm storage unit in a cloud platform;
a data storage instruction, which is used for acquiring a plurality of training data from an algorithm demand party and storing the training data in a data storage unit in the cloud platform, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a fetching instruction, which is used for fetching first training data from the plurality of training data stored in the data storage unit, and fetching a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
training instructions, training the first training data by using the first training algorithm container mirror image to obtain a training result;
and combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
While the present invention has been described in conjunction with specific embodiments, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, it is intended that such alternatives, modifications, and variations be included within the spirit and scope of the appended claims.
Claims (9)
1. A method of customizing an artificial intelligence online service, the method comprising:
the method comprises the steps of algorithm storage, wherein a plurality of algorithm data formats, a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images are obtained from an algorithm provider and stored in an algorithm storage unit in a cloud platform;
a data storage step, namely acquiring a plurality of training data from an algorithm demand party, and storing the training data in a data storage unit in the cloud platform, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a taking-out step of taking out first training data from the plurality of training data stored in the data storage unit, and taking out a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
training, namely training the first training data by using the first training algorithm container mirror image to obtain a training result;
and a combination step, combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
2. The method of customizing artificial intelligence online services according to claim 1, wherein the algorithm provider provides a plurality of artificial intelligence algorithms and the plurality of algorithm data formats, and modifies the plurality of artificial intelligence algorithms according to algorithm container mirror specifications provided by the cloud platform to obtain the plurality of training algorithm container mirrors and the plurality of online service algorithm container mirrors.
3. The method of customizing an artificial intelligence online service of claim 2, wherein the algorithm demander provides corresponding training data according to the corresponding algorithm data format selected from the plurality of algorithm data formats, thereby providing the plurality of training data.
4. The method of customizing an artificial intelligence online service of any one of claims 1-3, wherein the algorithm demander is unable to obtain a plurality of training algorithm container images and a plurality of online service algorithm container images from the algorithm storage unit, and the algorithm provider is unable to obtain the plurality of training data from the data storage unit.
5. A system for customizing artificial intelligence online service, which is characterized by comprising an algorithm provider, an algorithm demander and a cloud platform, wherein,
the cloud platform includes:
the algorithm storage unit acquires and stores a plurality of algorithm data formats, a plurality of training algorithm container mirror images and a plurality of online service algorithm container mirror images from the algorithm provider;
the data storage unit is used for acquiring a plurality of training data from the algorithm demand party and storing the training data, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a fetching unit that fetches first training data from the plurality of training data stored in the data storage unit, and fetches a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
the training unit is used for training the first training data by using the first training algorithm container mirror image to obtain a training result;
and the combination unit is used for combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
6. The system of customizing artificial intelligence online services of claim 5, wherein the algorithm provider provides a plurality of artificial intelligence algorithms and the plurality of algorithm data formats, and modifies the plurality of artificial intelligence algorithms according to algorithm container mirror specifications provided by the cloud platform to obtain the plurality of training algorithm container mirrors and the plurality of online service algorithm container mirrors.
7. The system of customizing an artificial intelligence online service of claim 6, wherein the algorithm demander provides corresponding training data according to the corresponding algorithm data format selected from the plurality of algorithm data formats, thereby providing the plurality of training data.
8. The system of customizing an artificial intelligence online service of any one of claims 5-7, wherein the algorithm demander is unable to obtain a plurality of training algorithm container images and a plurality of online service algorithm container images from the algorithm storage unit, and the algorithm provider is unable to obtain the plurality of training data from the data storage unit.
9. A non-volatile storage medium having stored thereon a program for customizing an artificial intelligence online service, the program being executed by a computer to implement a method for customizing an artificial intelligence online service, the program comprising:
the method comprises the steps that an algorithm storage instruction is obtained from an algorithm provider, and a plurality of algorithm data formats, a plurality of training algorithm container images and a plurality of online service algorithm container images are stored in an algorithm storage unit in a cloud platform;
a data storage instruction, which is used for acquiring a plurality of training data from an algorithm demand party and storing the training data in a data storage unit in the cloud platform, wherein the data formats of the training data respectively accord with the corresponding algorithm data formats in the algorithm data formats;
a fetching instruction, which is used for fetching first training data from the plurality of training data stored in the data storage unit, and fetching a first training algorithm container mirror image and a first online service algorithm container mirror image corresponding to the first training data from the plurality of training algorithm container mirror images and the plurality of online service algorithm container mirror images stored in the algorithm storage unit;
training instructions, training the first training data by using the first training algorithm container mirror image to obtain a training result;
and combining the first online service algorithm container mirror image and the training result to obtain the customized artificial intelligence online service.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1550977A1 (en) * | 2003-12-26 | 2005-07-06 | Hiroshima University | Reference data recognition and learning method and pattern recognition system |
| CN102681899A (en) * | 2011-03-14 | 2012-09-19 | 金剑 | Virtual computing resource dynamic management system of cloud computing service platform |
| CN103543980A (en) * | 2013-11-07 | 2014-01-29 | 吴胜远 | Digital data processing method and device |
| CN105931105A (en) * | 2016-04-26 | 2016-09-07 | 浙江喊喊网络科技有限公司 | Online service customization system and use method thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140279770A1 (en) * | 2013-03-15 | 2014-09-18 | REMTCS Inc. | Artificial neural network interface and methods of training the same for various use cases |
-
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- 2018-08-01 CN CN201810866825.XA patent/CN109034254B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1550977A1 (en) * | 2003-12-26 | 2005-07-06 | Hiroshima University | Reference data recognition and learning method and pattern recognition system |
| CN102681899A (en) * | 2011-03-14 | 2012-09-19 | 金剑 | Virtual computing resource dynamic management system of cloud computing service platform |
| CN103543980A (en) * | 2013-11-07 | 2014-01-29 | 吴胜远 | Digital data processing method and device |
| CN105931105A (en) * | 2016-04-26 | 2016-09-07 | 浙江喊喊网络科技有限公司 | Online service customization system and use method thereof |
Non-Patent Citations (2)
| Title |
|---|
| M_ICT应用发展趋势及其关键技术分析;吕达 等;《中兴通讯技术》;20170306;第23卷(第2期);第50-55页 * |
| 商业自动化决策的算法解释权研究;张凌寒;《法律科学（西南政法大学学报）》;20180331(第3期);第65-74页 * |
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